Refining satellite Altimetry-Derived gravity anomaly model with shipborne gravity using multilayer perceptron neural networks
Abstract This study refines the gravity anomaly model derived from altimetry data by employing a multilayer perceptron (MLP) neural network to integrate multi-source geophysical data (longitude, latitude, gravity anomaly, geoid height, bathymetry, and sediment thickness) based on shipborne gravity....
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Nature Portfolio
2025-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-04619-8 |
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| author | Chengjun Xiao Jinyun Guo Chengcheng Zhu Hui Li Shangguo Liu Xin Liu |
| author_facet | Chengjun Xiao Jinyun Guo Chengcheng Zhu Hui Li Shangguo Liu Xin Liu |
| author_sort | Chengjun Xiao |
| collection | DOAJ |
| description | Abstract This study refines the gravity anomaly model derived from altimetry data by employing a multilayer perceptron (MLP) neural network to integrate multi-source geophysical data (longitude, latitude, gravity anomaly, geoid height, bathymetry, and sediment thickness) based on shipborne gravity. To reduce the impact of land on gravity anomaly inversion, the experimental area is divided into nearshore and offshore regions, with separate inversions for each. The model is trained using differences between shipborne gravity control points and 8′×8′ grid points as input data, and differences between control point gravity anomalies and SDUST2022GRA model values as output data. The trained model predicts gravity anomalies at grid centers, and SDUST2022GRA values are applied to restore the predicted anomalies. The Gulf of Mexico region (81°W–99°W, 15°N–32°N) is selected to establish a high-resolution (1′×1′) MLP Gravity Anomaly model (MLP_GRA). Compared to the SDUST2022GRA, SIO_V32.1, and DTU21GRA models, the MLP_GRA model reduces the standard deviation (STD) and mean absolute error (MAE) by 0.4 mGal and 0.32 mGal, 0.54 mGal and 0.37 mGal, and 0.39 mGal and 0.27 mGal, respectively. These results confirm the reliability and effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-cffbb973f1134207b313d367576cece0 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-06-01 |
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| spelling | doaj-art-cffbb973f1134207b313d367576cece02025-08-20T02:05:48ZengNature PortfolioScientific Reports2045-23222025-06-0115111310.1038/s41598-025-04619-8Refining satellite Altimetry-Derived gravity anomaly model with shipborne gravity using multilayer perceptron neural networksChengjun Xiao0Jinyun Guo1Chengcheng Zhu2Hui Li3Shangguo Liu4Xin Liu5College of Geodesy and Geomatics, Shandong University of Science and TechnologyCollege of Geodesy and Geomatics, Shandong University of Science and TechnologySchool of Surveying and Geo-Informatics, Shandong Jianzhu UniversitySchool of Marine Science and Technology, Northwestern Polytechnical UniversityCollege of Geodesy and Geomatics, Shandong University of Science and TechnologyCollege of Geodesy and Geomatics, Shandong University of Science and TechnologyAbstract This study refines the gravity anomaly model derived from altimetry data by employing a multilayer perceptron (MLP) neural network to integrate multi-source geophysical data (longitude, latitude, gravity anomaly, geoid height, bathymetry, and sediment thickness) based on shipborne gravity. To reduce the impact of land on gravity anomaly inversion, the experimental area is divided into nearshore and offshore regions, with separate inversions for each. The model is trained using differences between shipborne gravity control points and 8′×8′ grid points as input data, and differences between control point gravity anomalies and SDUST2022GRA model values as output data. The trained model predicts gravity anomalies at grid centers, and SDUST2022GRA values are applied to restore the predicted anomalies. The Gulf of Mexico region (81°W–99°W, 15°N–32°N) is selected to establish a high-resolution (1′×1′) MLP Gravity Anomaly model (MLP_GRA). Compared to the SDUST2022GRA, SIO_V32.1, and DTU21GRA models, the MLP_GRA model reduces the standard deviation (STD) and mean absolute error (MAE) by 0.4 mGal and 0.32 mGal, 0.54 mGal and 0.37 mGal, and 0.39 mGal and 0.27 mGal, respectively. These results confirm the reliability and effectiveness of the proposed method.https://doi.org/10.1038/s41598-025-04619-8Multilayer perceptronMarine gravity anomaliesShipborne gravityMulti-source geophysical dataGulf of Mexico |
| spellingShingle | Chengjun Xiao Jinyun Guo Chengcheng Zhu Hui Li Shangguo Liu Xin Liu Refining satellite Altimetry-Derived gravity anomaly model with shipborne gravity using multilayer perceptron neural networks Scientific Reports Multilayer perceptron Marine gravity anomalies Shipborne gravity Multi-source geophysical data Gulf of Mexico |
| title | Refining satellite Altimetry-Derived gravity anomaly model with shipborne gravity using multilayer perceptron neural networks |
| title_full | Refining satellite Altimetry-Derived gravity anomaly model with shipborne gravity using multilayer perceptron neural networks |
| title_fullStr | Refining satellite Altimetry-Derived gravity anomaly model with shipborne gravity using multilayer perceptron neural networks |
| title_full_unstemmed | Refining satellite Altimetry-Derived gravity anomaly model with shipborne gravity using multilayer perceptron neural networks |
| title_short | Refining satellite Altimetry-Derived gravity anomaly model with shipborne gravity using multilayer perceptron neural networks |
| title_sort | refining satellite altimetry derived gravity anomaly model with shipborne gravity using multilayer perceptron neural networks |
| topic | Multilayer perceptron Marine gravity anomalies Shipborne gravity Multi-source geophysical data Gulf of Mexico |
| url | https://doi.org/10.1038/s41598-025-04619-8 |
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